List <Individual> BreedPopulation()
{
List <Individual> newpop = new List <Individual>();
//ELITISMO
population.Sort((x, y) => x.fitness.CompareTo(y.fitness));
for (int i = 0; i < numElit; i++)
{
newpop.Add(population [i]);
}
//breed individuals and place them on new population. We'll apply crossover and mutation later
while (newpop.Count < populationSize)
{
int count = 0;
Individual selected1 = tournamentSelection.selectIndividuals(population, maxTournament); //we should propably always select pairs of individuals
Individual selected2 = tournamentSelection.selectIndividuals(population, maxTournament); //we should propably always select pairs of individuals
if (newpop.Count < populationSize)
{
newpop.Add(selected1); //added individuals are already copys, so we can apply crossover and mutation directly
count++;
}
if (newpop.Count < populationSize)
{
newpop.Add(selected2); //added individuals are already copys, so we can apply crossover and mutation directly
count++;
}
//apply crossover between pairs of individuals and mutation to each one
if (count > 1)
{
selected1.Crossover(selected2, crossoverProbability);
selected1.Mutate(mutationProbability);
selected2.Mutate(mutationProbability);
}
if (count == 1)
{
selected1.Mutate(mutationProbability);
}
}
return(newpop);
}
//The Step function assumes that the fitness values of all the individuals in the population have been calculated.
public virtual void Step()
{
if (generation < numGenerations)
{
List <Individual> new_pop;
//Store statistics in log, and sorts population
stats.GenLog(population, generation);
population.Sort((x, y) => y.Fitness.CompareTo(x.Fitness));
//List<Individual> aux = new List<Individual> ();
int new_pop_size = populationSize - elitismAffected;
//Select parents
new_pop = selection.selectIndividuals(population, new_pop_size);
//Crossover par/impar
if (new_pop_size % 2 == 1)
{
for (int i = 0; i < new_pop_size - 1; i += 2)
{
Individual parent1 = new_pop [i];
Individual parent2 = new_pop [i + 1];
parent1.Crossover(parent2, crossoverProbability, ncortes);
}
}
else
{
for (int i = 0; i < new_pop_size; i += 2)
{
Individual parent1 = new_pop [i];
Individual parent2 = new_pop [i + 1];
parent1.Crossover(parent2, crossoverProbability, ncortes);
}
}
//Mutation and Translation
for (int i = 0; i < new_pop_size; i++)
{
new_pop [i].Mutate(mutationProbability);
new_pop [i].Translate();
}
//Select new population
for (int i = 0; i < elitismAffected; i++)
{
new_pop.Add(population [i]);
}
new_pop.Sort((x, y) => y.Fitness.CompareTo(x.Fitness));
population = new_pop;
generation++;
}
}
//The Step function assumes that the fitness values of all the individuals in the population have been calculated.
public virtual void Step()
{
if (generation < numGenerations)
{
List <Individual> new_pop;
//Store statistics in log
stats.GenLog(population, generation);
//Select parents
new_pop = selection.selectIndividuals(population, populationSize - preservedIndividualsElitism);
//Crossover
for (int i = 0; i < populationSize - preservedIndividualsElitism; i += 2)
{
Individual parent1 = new_pop [i];
Individual parent2 = new_pop [i + 1];
parent1.n_cuts = N_cutsCrossover;
parent1.CrossoverType = (int)crossoverType;
parent1.Crossover(parent2, crossoverProbability);
}
//Mutation and Translation
for (int i = 1; i < populationSize - preservedIndividualsElitism; i++)
{
//Debug.Log ("MutationType = " + mutationType + "/nIn int = " + (int)mutationType);
new_pop [i].MutationType = (int)mutationType; //send public modifier mutationType to Individual
new_pop [i].Mutate(mutationProbability);
new_pop [i].Translate();
}
//ELITISM
//List<Individual> elitismIndividuals = new List<Individual> ();
//sort population by fitness
population.Sort((x, y) => y.Fitness.CompareTo(x.Fitness));
for (int i = 0; i < preservedIndividualsElitism; i++)
{
//Debug.Log(population[i].Fitness);
new_pop.Add(population [i]);
}
//END ELITISM
//Select new population
population = new_pop;
generation++;
}
}